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A Clustering-Based Multiple Kernel Learning Algorithm for Multi-Class-Review Article Classification

Zhang Xiaofeng*

Multiple kernel learning algorithms typically optimize kernel alignment, structural risk minimization, and Bayesian functions. However, they have limitations, including inapplicability to multi-class classification, high time complexity, and no analytic solution. Analyzing clustering and classification similarities, we propose a novel Clustering-Based Multiple Kernel Learning (CBMKL) algorithm for multi-class classification. This algorithm transforms input space to high-dimension feature space using multiple kernel mapping functions. It estimates base kernel function weights and constructs the decision function using clustering objectives. This CBMKL algorithm has several advantages.

• It handles multi-class problems directly.

• This algorithm has an analytical solution, avoiding approximate solutions from sampling methods.

• It also has polynomial time complexity. Experiments on two datasets illustrate these advantages.